I am driven by two great passions –the aesthetics of parsimonious models for complex phenomena, and a strong desire to understand the different ways by which we perceive, reason, interact, and create.

In the quest to understand the mind and the brain, one should cover many levels of inquiry: from single neurons, to groups of neurons and their interactions, regions and networks of concerted activity and interactions in the brain, and behavior. Luckily, we live in exciting times where data from all these levels is recorded at an unprecedented spatial and temporal resolution. In our lab, we seek to first understand the computational goals of cognitive processes and by which to infer their computational function and design principles. In turn, these allow to understand the computations and to seek its implementation in the brain. We study creativity, social motion, predictive coding in geometry and physics, and computational trade-off in health and disease with a current focus on autism spectrum disorder.

We employ a range of tools from statistical physics, dynamical systems, functional data analysis, and "big-data" analysis on high-resolution measurements to reveal basic computational principles of complex cognitive phenomena.

I'm also highly passionate about education, and see it as a social tool for progress. In my teachings I hope to improve the impact of classroom and online courses on students.